Predicting Nitrogen, Phosphorus, and Potassium Content in Dryland Agriculture Soils of Aceh Besar District Using NIRS Data Models
Bibliographic record
Abstract
This research aims to develop a Near Infrared Reflectance Spectroscopy (NIRS) model to determine the content of macro nutrients in dry agricultural land.The macro nutrient content is needed by plants to grow ideally.Every plant requires large amounts of macro nutrients such as nitrogen (N), phosphorus (P), and potassium (K).NIR spectrum data were collected from 30 samples in the range of 400-1100 nm using the Nicolet-Antaris device.The dual spectrum techniques used were Standard Normal Variate (SNV) and Mean Normalization (MN).This pre-treatment is chosen based on its function and aims to reduce or eliminate noise in the resulting spectrum.The corrected spectrum pattern helps to reduce discrepancies between spectral bands.The fewer gaps in the spectrum, the more accurate the resulting model.Next, the Partial Least Squares (PLS) and Principal Component Regression (PCR) algorithms are used to form a validation model.This multivariate analysis was carried out using the Unscrambler X 10.3 software.Model reliability is assessed using a number of statistical measures: correlation coefficient (r), coefficient of determination (R 2 ), root mean square error (RMSE) and range of error ratio (RER).The best model was taken based on previous findings when applying PCR to the MN-normalized spectra which was superior in predicting nitrogen and potassium content, while the Phosphorus content was superior when applying PCR with the SNV normalized spectrum technique.These findings show that NIRS combined with chemometric can be used to predict the nutritional content of nitrogen, Phosphorus and potassium quickly and simultaneously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".